We evaluate ForceNet on OC20 [1]}, a recently-introduced large-scale dataset of quantum physics calculations with 200+ million large atomic structures (20–200 atoms) useful for discovering new catalysts for energy applications [2]}, [3]}, [4]} (Figure REF ).
The dataset was constructed with an unprecedented 70 million CPU hours of compute performing Density Functional Theory (DFT)-based quantum calculations [5]}—more than 20 times the compute as compared to the conventional quantum physics datasets of QM9 [6]} and Alchemy [7]}, making it ideal for scalable deep learning approaches.
Moreover, unlike many existing quantum physics datasets, OC20 provides non-equilibrium structures of molecules, i.e., 3D structure with non-zero atomic forces, making it a good testbed of our model.
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